干旱区生态环境知识资源中心(Arid): A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods

Arid

DOI	10.1016/j.apgeog.2023.103035
	A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods
	Shikhteymour, Sharareh Rashidi; Borji, Moslem; Bagheri-Gavkosh, Mehdi; Azimi, Ehsan; Collins, Timothy W.
通讯作者	Collins, TW
来源期刊	APPLIED GEOGRAPHY
ISSN	0143-6228
EISSN	1873-7730
出版年	2023
卷号	158
英文摘要	Hazardous flooding occurs across most climate zones. Owing to the lack of appropriate infrastructures and applicable predictive methods, flooding in arid and semi-arid regions may be especially damaging. Based on a study of Abarkuh County, Iran, we introduce an integrated approach for identifying high-priority flood risk areas using machine learning (ML) and multi-criteria decision-making (MCDM) methods, which is transferable to other (semi)arid regions. Results indicate that among the ML models we examined-including classification and regression tree (CART), mixture discriminant analysis (MDA), and support vector machine (SVM)-the SVM model performs best. We estimate that 75% of the study area is subject to high or very flood hazard. Our application of the Jackknife technique identifies precipitation, vegetation, and drainage density as the most important conditional factors for regional flood hazards. Our analytical network process (ANP)-decision making trial and evaluation laboratory (DEMATEL) results reveal that population density and agricultural area density have the greatest influence on flood vulnerability. Results integrating SVM and ANP-DEMATEL flood hazard and vulnerability maps indicate that 6% of the study area is at high or very high flood risk. Application of this approach can assist local authorities in identifying priority areas for flood management interventions.
英文关键词	Flood Hazard Vulnerability Arid and semi-arid regions Risk zone
类型	Article
语种	英语
收录类别	SSCI
WOS记录号	WOS:001044620400001
WOS关键词	SUSCEPTIBILITY ASSESSMENT ; NEURAL-NETWORK ; MODEL ; VULNERABILITY ; FRAMEWORK ; HAZARD
WOS类目	Geography
WOS研究方向	Geography
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395349
推荐引用方式 GB/T 7714	Shikhteymour, Sharareh Rashidi,Borji, Moslem,Bagheri-Gavkosh, Mehdi,et al. A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods[J],2023,158.
APA	Shikhteymour, Sharareh Rashidi,Borji, Moslem,Bagheri-Gavkosh, Mehdi,Azimi, Ehsan,&Collins, Timothy W..(2023).A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods.APPLIED GEOGRAPHY,158.
MLA	Shikhteymour, Sharareh Rashidi,et al."A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods".APPLIED GEOGRAPHY 158(2023).